SPATIO‐TEMPORAL VARIATIONS OF SEVEN WEATHER VARIABLES IN IRAN: APPLICATION OF CRU TS AND GPCC DATA SETS^†

Abstract

Iran's climate‐sensitive agriculture and water resources are vulnerable to climate change and investigation of climatic trends helps in preparing adaptation strategies. Weather stations are sparsely distributed and access to complete weather data is limited. In such situations, gridded global/regional data sets are promising alternatives. Here, monthly time series of seven weather variables (i.e. monthly averages or monthly totals of daily values) were obtained from the Climatic Research Unit TS V4.01 and Global Precipitation Climatology Centre V7 gridded data sets in 675 grid cells covering the country and analysed over the periods 1957–1986 and 1987–2016 at annual, seasonal and monthly scales. Over the two periods and at a national scale, mean temperature has increased by 0.004 (P = 0.717) and 0.04 °C yr−1 (P = 0.000), while the diurnal temperature range has not significantly changed (P > 0.6). Annual total precipitation experienced an insignificant increase (0.81 mm yr−1; P = 0.666) over the first period but declined by 2.12 mm yr−1 (P = 0.041) over the second. Potential evapotranspiration (PET) has increased by 0.32 (P = 0.398) and 1.43 mm yr−1 (P = 0.015), respectively. Since 1987, significant increasing trends in temperature were detected all over the country. While significant increasing trends in annual precipitation were detected in the central regions and south‐west over the first period, decreasing trends prevailed during 1987–2016 in the south, southwest and east with winter being the largest contributor to annual trends. Over the last three decades, annual PET has increased mostly in the north‐west and south‐east while significant increasing trends were detected in 89% of grid cells, except in a few cells in the north‐east. Cloud cover, vapour pressure and frequency of frost days were also analysed. These results are crucial for policy‐makers, researchers and engineers in the country and internationally who usually base their decisions and designs on outdated data sparsely distributed in space. © 2020 John Wiley & Sons, Ltd.

Quantifying groundwater sensitivity and resilience over peninsular India

“…PET is evaluated using a version of Penman‐Monteith equation (FAO, 2020), which considers max temperature, minimum temperature, vapour pressure, cloud cover and wind velocity as inputs (Harris et al, 2014). This dataset has been pervasively used in recent literature (Ababaei, 2020; Sinha et al, 2018). Further, gridded datasets are spatially averaged at a catchment scale (lumped approach) to obtain the precipitation data and potential evapotranspiration data for respective catchment.…”

“…This dataset has been pervasively used in recent literature (Ababaei, 2020;Sinha et al, 2018). Further, gridded datasets are spatially averaged at a catchment scale (lumped approach) to obtain the precipitation data and potential evapotranspiration data for respective catchment.…”

Frequency of compound hot–dry weather extremes has significantly increased in Australia since 1889

“…The test has been frequently adopted in hydrological and meteorological studies (e.g. Tabari and Hosseinzadeh Talaee, 2011;Gocic and Trajkovic, 2013;Khalili et al, 2016;Araghi et al, 2018;Ababaei and Chenu, 2019;Ababaei and Ramezani Etedali, 2019;Ababaei, 2020;Alizadeh et al, 2020) and its calculation procedure can be found in Ababaei and Chenu (2020). The accuracy of the test is affected by the presence of serial correlation.…”

“…Great many climate risk studies have analysed the univariate hydroclimatic extremes. It has been shown that heatwave magnitudes, frequencies, intensities, and spatial extents are increasing in Australia (Ababaei and Chenu, 2020) and other regions of the world (Pai et al, 2013;Cheng et al, 2014;Sun et al, 2014;Christidis et al, 2015;Ramezani Etedali et al, 2018;Ababaei and Ramezani Etedali, 2019;Ababaei, 2020), a trend that is projected to continue in a warming climate (Lobell et al, 2015;Murari et al, 2015;Alexander and Arblaster, 2017;Zscheischler et al, 2018;Trancoso et al, 2020). Moreover, analyses of historical precipitation, streamflow, and soil moisture indices show an increasing trend of aridity over many regions across the world (Hennessy et al, 1999;Vicente-Serrano et al, 2010;Wang et al, 2011;Mishra and Liu, 2014;Mallya et al, 2015;Araghi et al, 2018;Ababaei and Ramezani Etedali, 2019;Ababaei, 2020).…”

“…Great many climate risk studies have analysed the univariate hydroclimatic extremes. It has been shown that heatwave magnitudes, frequencies, intensities and spatial extents are increasing in Australia (Ababaei & Chenu, 2020) and other regions of the world (Ababaei, 2020; Ababaei & Ramezani Etedali, 2019; Cheng et al., 2014; Christidis et al., 2015; Pai et al., 2013; Ramezani Etedali et al., 2018; Sun et al., 2014), a trend that is projected to continue in a warming climate (Alexander & Arblaster, 2017; Lobell et al., 2015; Murari et al., 2015; Trancoso et al., 2020; Zscheischler et al., 2018). Moreover, analyses of historical precipitation, streamflow and soil moisture indices show an increasing trend of aridity over many regions across the world (Ababaei, 2020; Ababaei & Ramezani Etedali, 2019; Araghi et al., 2018; Hennessy et al., 1999; Mallya et al., 2015; Mishra & Liu, 2014; Vicente‐Serrano et al., 2010; Wang et al., 2011).…”